Discover thousands of Public Tenders around the world!
A large number of public tenders are published every year around the world. This generates a big opportunity for Life Sciences companies, which make between 30-80% of their revenue from this market.
Tenders are also a great source of information including competitive intelligence, pricing, volumes award values, decision-making criteria with a breakdown per market, product category, molecule, therapeutic area, and contracting authorities.
In order to identify the most relevant opportunities and relevant tender or award insights, we need to extract data from unstructured tender information, and this is where Natural Language Processing (NLP) plays an important role.
Automate Data Gathering using NLP (Natural Language Processing) for tenders
NLP or Natural Language Processing is an Artificial Intelligence technology, creating structured information out of unstructured textual content. The resulting data can then be used to provide insights, automatically filling missing information and train predictive models.
More specifically, NLP algorithms can be used to automate the following processes:
- Smart Search for Keywords based on molecules, therapeutic areas, disease, indications, product category, or other relevant tender information, even when these are mentioned in similar terms, misspelled, or translated in other languages.
- Extract additional tender or award information such as win price, awardee name, volumes, lots, criteria, contract duration or other information.Map contracting authorities, to existing accounts
This significantly improves searching accuracy, user experience, reduces time & effort, and reveals more information for every tender that may not be readily available otherwise.
Automatic Tender discovery with Cube RM Tender Central
Cube RM Tender Central can automatically connect to multiple online tender sources, such as TED (Tender Electronic Daily), and consolidate all information into a single source of truth. It can also identify relevant tenders and lots, leveraging NLP and search automatically for molecules, therapeutic areas, disease, indications, product category, or other keywords. Furthermore, the user can filter incoming tenders by using release & submission dates, budget, or other criteria.
Advanced bid-no-bid tender guidance
Through the Qualification Index, all available tenders and lots are scored based on various weighted criteria, such as product categories, keywords, budget, and other tender information resulting in a five-star ranking from more to less important tenders. The Qualification Index can be enriched with more sophisticated criteria, such as the available supply capacity for the requested products, expected profitability, past success rates, and strategic importance of the customer or products.
The system can use these or other criteria to score tenders and identify the most attractive ones, worthy to further pursue and invest more time and resources.